Our proposed A2r has advantages beyond alleviating interlocking.
Recent work [1]}, [2]} pointed out the lack of inherent interpretability in rationalization models, because the black-box generators are not guaranteed to produce causally corrected rationales.
Our A2r framework can alleviate this problem as the soft training path and the attention-based rationale generation improves the interpretability, which suggests a potential towards fully interpretable rationalization models in the future.
